A Minimax Probability Machine for Nondecomposable Performance Measures

doi:10.1109/TNNLS.2021.3106484

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	A Minimax Probability Machine for Nondecomposable Performance Measures
	Luo, Junru 1,3; Qiao, Hong 2,4; Zhang, Bo 5,6,7
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-09-01
页码	13
关键词	Measurement Task analysis Covariance matrices Support vector machines Prediction algorithms Minimization Kernel Imbalanced classification minimax probability machine nondecomposable performance measures
ISSN号	2162-237X
DOI	10.1109/TNNLS.2021.3106484
英文摘要	Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate (AR), it is usually much more appropriate to use nondecomposable performance measures such as the area under the receiver operating characteristic curve (AUC) and the $F_beta$ measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the AR, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this article is to develop a new minimax probability machine for the $F_beta$ measure, called minimax probability machine for the $F_beta$ -measures (MPMF), which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other nondecomposable performance measures listed in the article. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model.
资助项目	NNSF of China[91948303] ; NNSF of China[61627808]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000732226800001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/59727]
专题	应用数学研究所
通讯作者	Zhang, Bo
作者单位	1.Changzhou Univ, Aliyun Sch Big Data, Changzhou 213100, Jiangsu, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 3.Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou 213100, Jiangsu, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 7.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Luo, Junru,Qiao, Hong,Zhang, Bo. A Minimax Probability Machine for Nondecomposable Performance Measures[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:13.
APA	Luo, Junru,Qiao, Hong,&Zhang, Bo.(2021).A Minimax Probability Machine for Nondecomposable Performance Measures.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13.
MLA	Luo, Junru,et al."A Minimax Probability Machine for Nondecomposable Performance Measures".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):13.